We are Odoo + DevOps specialists based in Argentina. We design and operate high-performance infrastructure on VPS or Kubernetes with end-to-end proxying, caching, pooling, and observability.

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Benefits (UX-centric, load time & iteration)

User Experience and Conversion

•  Snappier interactions: click-to-response in 300–700 ms on common views (list/kanban/form) to reduce user friction.
• POS that feels instant during peaks—faster scans, taxes, and discounts with no stalls.
• Stable navigation across many tabs/users: fewer disconnects and session drops.

Faster checkout in e-commerce: lower abandonment due to long waits → conversion uplift.

Speed & Load Times

• Lower p95 latency on critical endpoints (searches, large lists, reports).
• 2–5× faster imports/exports via pooling, indexing, and right-sized batches.
• Shorter “time-to-first-data” for big lists using smart pagination + cache (Redis/Heimdall).
• Read/Write Split (PGCat): offload reads to replicas without slowing primary writes.

Operational Reliability

• Fewer timeouts & MemoryErrors in period closes and heavy reports (worker/memory limits tuned).
• Planned zero-downtime for upgrades/deploys (Kubernetes or well-tuned VPS).
• Fast recovery with verified backups and restore/DR playbooks.

Iteration Speed (DevOps)

• Shorter release cycles: CI/CD pipelines with tests, migrations, environment-based deploys.
• Per-branch previews (feature staging) for feedback in hours, not weeks.
• Safe rollbacks with change windows and measurable guardrails.

Observability & Support

• Actionable dashboards in Grafana (latency, throughput, error rates) + logs/traces in OpenSearch.
• Early warnings (CPU spikes, locks, delayed autovacuum) before users feel pain.
• Load testing with Locust: measured capacity and evidence-based bottleneck fixes.

Cost Efficiency

• Lower CPU/IO per transaction (PgBouncer/Redis/indexing) → more users per server.
• Scale gradually: add read replicas & cache before over-provisioning hardware.
• Predictable spend with SLOs and minimum performance targets per key flow.

One-liners for highlight cards

• −40–70% p95 latency on key views
• 2–5× import/export throughput
• 0 planned downtime on deploys
• MTTR ↓ thanks to metrics + centralized logs
• More users / same hardware

 10 keys of our service

1. 10-Point Performance Audit
   Infra (CPU/RAM/IO), Postgres, Odoo, Nginx/SSL, queues/cron, modules, logs, networking, baseline security.
2. Postgres Tuning
   shared_buffers, work_mem, effective_cache_size, autovacuum, indexing, and critical execution plans.
3. Pooling and Read/Write Split
   PgBouncer, PGCat, and Heimdall alternative to split reads, keep connections, and cut latency.
4. Caching and Proxy
   Redis (cache + sessions), Nginx (gzip, keep-alive, HTTP/2, longpolling), security and policies.
5. Kubernetes / VPS
   Reproducible deployments, healthchecks, auto-healing, HPA, affinity and backups.
6. CI/CD Pipelines
   GitHub/GitLab Actions: tests, builds, Docker images, migrations, and environment-based deploys.
7. Migrations
   Data (safe ETL) and modules (cross-version), validations, testing, and rollback plans.
8. Observability & Metrics
    Grafana (dashboards), OpenSearch (logs/traces), alerts, and business KPIs.
9. Load Testing
   Locust: realistic scenarios (POS, checkout, picking, invoicing) with reports and recommendations.
10. Hardening & Reliability
    Verified backups, fast restore, DR plan, resource limits, and endpoint security.
    
    

Key stack, explained simply

• PGanalyze: heavy queries, missing indexes, autovacuum insights → fewer seq scans & better plans.
• PgBouncer: transaction/session pooling → thousands of logical conns without overloading Postgres.
• PGCat: read/write split to primary/replicas with tunable consistency → scale reads safely.
• Heimdall (optional, enterprise): smart proxy with query caching → fewer DB hits, faster lists.
• Redis: data cache and Odoo sessions → lighter CPU/DB load and smoother UX.
• Nginx: reverse proxy for HTTP + longpolling, compression and secure headers → efficient delivery.
• Kubernetes: replicas, rolling updates, healthchecks → planned zero-downtime and resilience.
• Grafana + OpenSearch: dashboards, centralized logs, alerts → detect issues before users feel them.
• Locust: simulate spikes, find bottlenecks → measured capacity with evidence.
  
  

How we work (4 steps)  

1. Discover (Day 0-1) – Stack, metrics, pains.
2. Plan (Day 2-3) – Quick-wins + structural changes.
3. Implement (Day 3-10) – Tuning & automation with rollback.
4. Measure and Support (ongoing) – Dashboards, alerts, SLOs, monthly review.

Deliverables

• Findings & plan report.
• Dashboards (Grafana) and alerts ready to use.
• CI/CD pipelines with tests and migrations.
• Backup/restore & DR playbooks.
• Operations runbook for your team. 
  
  

Expected outcomes

• Lower latency in critical operations.
• Fewer memory/time errors in closings/reports.
• Real scalability for campaigns/peak seasons.
• Incident traceability in minutes.

Good vs. Bad Odoo Configuration (What Users Actually Feel)

Area	Well-Configured Odoo	Poorly Configured Odoo
UX & Latency	Click-to-response ~300–700 ms on common views; POS feels instant even at peak.	1.5–5 s delays on lists/forms; POS “freezes” on taxes/discounts; frustrated users.
Concurrency	Hundreds of users stable; sessions don’t drop; no “please try again”.	Random logouts, lost carts/quotes; “database is busy” at peak.
Reports & Closes	Heavy reports complete reliably; month-end closes predictable.	Timeouts/MemoryErrors; retries; closes spill into business hours.
Imports/Exports	2–5× faster with pooling, indexes, batch sizing.	CSV jobs stuck; workers starve the DB; backlogs build.
Longpolling (chats, bus)	Gevent + dedicated workers keep notifications realtime.	Notifications lag minutes; live features feel “dead”.
Deploys/Upgrades	Planned zero-downtime; quick rollbacks.	Downtime windows slip; “works on staging, breaks on prod”.
Observability	Grafana dashboards + OpenSearch logs = MTTR in minutes.	Guesswork; “it’s slow” without evidence; incidents drag on.
Cost	More users per server; scale with replicas/cache first.	Overprovisioning hides problems; higher bills for same throughput.